In the area of digital image processing and automated reading of text on digital images, the images often get thresholded (i.e., binarized) from a grayscale image to a binary image. Image binarization converts an image of up to 256 gray levels to a black and white image. Frequently, binarization is used as a pre-processor before optical character recognition (OCR) or intelligent character recognition (ICR). In fact, most OCR packages on the market work only on bi-level (black & white) images. The simplest way to use image binarization is to choose a threshold value, and classify all pixels with values above this threshold as white, and all other pixels as black. The problem then is how to select the correct threshold. In many cases, finding one threshold compatible to the entire image is very difficult, and in many cases even impossible.
For example, in the banking industry, areas of interest to be automatically read from a digital image of a personal check may include text in the magnetic ink character recognition (MICR) line of the check or the handwritten amount on the check. Often, people put checks in their pockets which causes fold lines on the check. These fold lines often come up as gray areas around objects of interest such as the (MICR) line of the check, the handwritten amount on the check, the payee, etc. When OCR or ICR software fails to read these areas a person must look at the check and manually key in these amounts. Also, this may cause difficulty in converting the check image to a binary image to be sent as an image cash letter for regulatory compliance. This results in more money being spent on people to manually review bad check images due to poor binarization conversion for certain check images.
In this regard, there is a need for systems and methods that overcome shortcomings of the prior art.